Abstract
In this paper, we present a study of the use of an artificial immune system (CLONALG) for solving constrained global optimization problems. As part of this study, we evaluate the performance of the algorithm both with binary encoding and with real-numbers encoding. Additionally, we also evaluate the impact of the mutation operator in the performance of the approach by comparing Cauchy and Gaussian mutations. Finally, we propose a new mutation operator which significantly improves the performance of CLONALG in constrained optimization.
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Cruz-Cortés, N., Trejo-Pérez, D., Coello, C.A.C. (2005). Handling Constraints in Global Optimization Using an Artificial Immune System. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_18
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DOI: https://doi.org/10.1007/11536444_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28175-7
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